Customers

Who hires you & what they get

Knowing who hires you and what they actually get is the context AI needs to help you communicate, create, and deliver for them well.

Customer understanding is not just for sales and marketing. Every piece of work you produce either serves a customer directly or connects to someone who does. A tool helping you write, plan, or decide needs to know who the work is ultimately for, or it will optimize for the wrong thing.

A customer description worth giving to AI covers who they are (their role, context, and level of familiarity with your domain), what they hired you or your team to do, and what a good result looks like from their perspective. That description shapes everything from tone to depth to format.

Customer context also changes over time. Who your customers were two years ago may not be who they are today, and what they expect has likely shifted too. Keeping the description current is part of keeping your AI context accurate.

Make it visibleWrite a one-paragraph description of your most important customer: who they are, what they hired you to do, what a good result looks like to them, and one thing about how they communicate that shapes how you work with them. Use this description as context whenever you are using AI to help with anything customer-facing.

Why AI needs this

Each part of your work matters to AI in a specific way. Some of it is context a tool needs before it can help, some of it is work a tool can take on, and some of it is judgment that should stay with you.

Who the work is really for

Every output a tool helps you produce should be shaped by who receives it. A tool that does not know your customer will default to a generic reader that fits no one well.

What good looks like for them

A customer's definition of a successful result is not always the same as a technically correct one. AI helping you plan or produce needs to understand what satisfaction looks like from the customer's side.

Their level of familiarity with your domain

How much a customer already knows shapes how much a tool should explain, what jargon is appropriate, and how formal the register should be. That calibration requires knowing the customer.

What they hired you for versus what you actually do

There is often a gap between what a customer thinks they bought and what you know you are providing. AI working on customer communication needs to understand both sides of that gap.

Questions to explore

Use these on your own or in a group. There are no right answers, only better conversations.

Who are your customers or clients, and what do they believe they are getting from you?
What does a great outcome look like from their perspective, not yours?
How much do they know about your domain, and how does that shape how you communicate with them?
Are there things customers consistently misunderstand about what you do, and how does that shape your communication?
Who inside a customer organization actually uses what you produce, and who decides whether to renew?

Readiness traps

Customer descriptions written for pitches and proposals tend to describe the ideal customer. The actual customer base often includes more variety, more constraint, and more skepticism than the pitch version.
B2B customer descriptions often stop at the company level and miss the individual: the person who reads the output, attends the call, and decides whether to renew. AI needs the individual level to calibrate tone and content.
Customer needs change. A description accurate eighteen months ago may not reflect what customers expect today, especially in domains where AI itself is changing what they can and cannot do.